INFERENCIA Y DESCUBRIMIENTO CASUAL EN PYTHON
MOLAK, ALEKSANDER
Verkauft von Gabo Books, COLUMBUS, OH, USA
AbeBooks-Verkäufer seit 19. Dezember 2024
Neu - Softcover
Zustand: Neu
Anzahl: 5 verfügbar
In den Warenkorb legenVerkauft von Gabo Books, COLUMBUS, OH, USA
AbeBooks-Verkäufer seit 19. Dezember 2024
Zustand: Neu
Anzahl: 5 verfügbar
In den Warenkorb legenEn comparación con el aprendizaje automático tradicional y las estadísticas, los métodos causales presentan desafíos únicos. Aprender causalidad puede ser difícil, pero ofrece distintas ventajas que escapan a una mentalidad puramente estadística. Este libro ayuda a liberar todo el potencial de la causalidad.
El libro comienza con las motivaciones básicas del pensamiento causal y una completa introducción a conceptos causales pearlianos, como los modelos causales estructurales, las intervenciones, los contrafactuales, etc. Cada concepto va acompañado de una explicación teórica y una serie de ejercicios prácticos con código Python. A continuación, entra de lleno en el mundo de la estimación del efecto causal, y avanza hacia los métodos de aprendizaje automático modernos.
Paso a paso, descubrirás el ecosistema causal de Python y aprovecharás la potencia de los algoritmos más avanzados. Además, explorarás la mecánica de las huellas que dejan las causas y descubrirás las cuatro familias principales de métodos de descubrimiento causal. El capítulo final ofrece una amplia visión general del futuro de la IA causal, con un examen de retos y oportunidades y una exhaustiva lista de recursos para seguir aprendiendo cada vez más.
Entre otras cosas, este libro permite:
* Dominar los conceptos fundamentales de la inferencia causal.
* Liberar el potencial del proceso de inferencia causal en cuatro pasos de Python.
* Explorar avanzadas técnicas de modelado uplift o de elevación.
* Descubrir los secretos del descubrimiento causal moderno con Python.
* Utilizar la inferencia causal para producir impacto social y beneficios para la comunidad.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
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